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AI Virtual Try-On for E-Commerce Clothing
This project designs and implements ai virtual try-on for e-commerce clothing by applying recommendation algorithms to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Powered Attendance System using Face Recognition
This project designs and implements attendance system by applying recommendation algorithms to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
Intelligent Email Spam & Phishing Classifier
This project designs and implements email spam & phishing classifier by applying computer vision + OCR pipelines to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
Sentiment Analysis Dashboard for Social Media Brand Monitoring
This project designs and implements sentiment analysis dashboard for social media brand monitoring by applying classical ML classifiers to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Based Fake News Detection System
This project designs and implements fake news detection system by applying graph-based similarity reasoning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI Chatbot for College Admission Enquiry Automation
This project designs and implements ai chatbot for college admission enquiry automation by applying transformer-based NLP to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Powered Legal Document Summarizer
This project designs and implements legal document summarizer by applying graph-based similarity reasoning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Powered Resume Screening & Candidate Ranking System
This project designs and implements resume screening & candidate ranking system by applying ensemble machine learning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Based Plagiarism Detection System for Academic Documents
This project designs and implements plagiarism detection system for academic documents by applying computer vision + OCR pipelines to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
Intelligent Customer Support Ticket Classification System
This project designs and implements customer support ticket classification system by applying classical ML classifiers to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
ambulance signal managing
Emergency vehicle delays due to traffic congestion remain a critical issue, often resulting in life-threatening situations. To address this challenge, this project presents an IoT and ML-based Ambulance Signal Management System that dynamically adjusts traffic signals to provide a clear path for ambulances. The system integrates NodeMCU and ESP32-CAM to detect approaching ambulances in real-time. The ESP32-CAM captures live video feeds at intersections, which are processed using machine learning algorithms deployed via a Flask-based backend to identify emergency vehicles with high accuracy. Upon detection, the system communicates with the traffic light control unit, mounted on a simulation stick setup, and automatically switches the signals to green in favor of the ambulanceâs route. This not only reduces response time but also minimizes manual intervention and traffic confusion. The system logs event data and provides a real-time dashboard for remote monitoring and analytics. The solution is scalable, efficient, and ideal for integration in smart traffic infrastructure to enhance emergency response efficiency in urban areas.
Smart Infant Incubator with Automated Oxygen and Temperature Control and Real-Time Alerts
Premature and critically ill infants require precise environmental conditions for survival and recovery. Traditional incubators often lack remote monitoring and intelligent control features, increasing the risk of manual error and delayed intervention. This project presents a Smart Infant Incubator System using IoT and Flask, designed for automated control of oxygen and temperature levels with real-time alerts to caregivers. The system is built around an ESP32 microcontroller, which continuously monitors vital parameters using a SpOâ sensor to track blood oxygen saturation and a temperature sensor to ensure thermal stability inside the incubator. Based on real-time sensor data, a solenoid valve regulates oxygen flow, while a water pump is used for humidification or temperature adjustment mechanisms within the chamber. All data is transmitted to a Flask-based IoT dashboard, enabling remote visualization, threshold-based alerts, and historical trend analysis. This intelligent incubator improves neonatal care by reducing the need for constant manual supervision and enhancing response time during critical fluctuations in an infantâs condition.